How to use a loop to work out a model through several variables in R
Question
Here my data for boosting
new=structure(list(B1 = c(6914L, 6914L, 6914L, 6958L, 6958L, 6958L,
6958L, 6914L, 6914L, 6914L, 6914L, 5672L, 6014L, 6014L, 6014L,
6014L, 6014L, 6958L, 6958L, 6958L, 6958L, 6958L, 6958L, 6914L,
6914L, 6914L, 6914L, 6092L, 6092L, 6092L, 6092L, 6092L, 6239L,
6239L, 6239L, 6239L, 6239L, 6239L, 6615L, 6615L, 6615L, 6615L,
6615L, 7038L, 7038L, 7038L, 7038L, 7038L, 7038L, 6602L, 8136L,
8136L, 7369L, 8136L, 8136L, 7369L, 8136L, 8136L, 8136L, 7369L,
7369L, 8136L, 8136L, 8136L, 8136L, 7369L, 7369L, 8136L, 8136L,
8136L, 8136L, 8317L, 8317L, 8317L, 8821L, 8821L, 8821L, 8821L,
8821L, 8317L, 8317L, 8821L, 8821L, 8821L, 8821L, 8821L, 8821L,
8317L, 8821L, 8821L, 8821L, 8821L, 8821L, 8821L, 8821L, 8821L,
8821L, 8821L, 8821L, 9245L), B2 = c(5560L, 5380L, 5644L, 5088L,
5280L, 5200L, 5472L, 5568L, 5560L, 5424L, 5404L, 4784L, 4696L,
4820L, 4588L, 4544L, 4452L, 4716L, 5048L, 5236L, 5416L, 5584L,
5824L, 5800L, 5932L, 5980L, 6112L, 4796L, 4860L, 5396L, 5900L,
5968L, 5968L, 5776L, 5440L, 5248L, 4884L, 4760L, 4796L, 4860L,
4776L, 4664L, 4716L, 4952L, 5168L, 5316L, 5548L, 5768L, 5900L,
5948L, 5796L, 5756L, 5912L, 5956L, 6000L, 6196L, 6044L, 6164L,
6268L, 6588L, 6508L, 6460L, 6608L, 6592L, 6600L, 7100L, 7016L,
6988L, 6952L, 6800L, 6644L, 7308L, 7340L, 7528L, 7492L, 7304L,
6928L, 6748L, 6764L, 7492L, 7648L, 7580L, 7416L, 7108L, 6864L,
7056L, 7164L, 7744L, 7720L, 7360L, 7188L, 7204L, 7280L, 7236L,
7520L, 7352L, 7352L, 7376L, 7320L, 7428L), B3 = c(4768L, 4840L,
4936L, 4320L, 4388L, 4572L, 4640L, 4704L, 4696L, 4488L, 4396L,
4002L, 4030L, 3960L, 3684L, 3680L, 3896L, 4212L, 4364L, 4508L,
4732L, 4896L, 4848L, 4872L, 4960L, 5052L, 4848L, 4308L, 4800L,
5216L, 5224L, 5248L, 5136L, 4720L, 4428L, 4120L, 3918L, 4052L,
4058L, 3806L, 3802L, 3930L, 4092L, 4324L, 4476L, 4604L, 4768L,
4980L, 5080L, 5288L, 4840L, 4936L, 5096L, 5040L, 5096L, 5292L,
5280L, 5360L, 5480L, 5584L, 5528L, 5524L, 5700L, 5736L, 5732L,
6136L, 5980L, 5884L, 5904L, 5820L, 5864L, 6488L, 6572L, 6476L,
6256L, 6024L, 5844L, 5884L, 6100L, 6684L, 6596L, 6376L, 6188L,
5952L, 6044L, 6212L, 6268L, 6668L, 6484L, 6336L, 6164L, 6332L,
6432L, 6396L, 6592L, 6548L, 6500L, 6464L, 6460L, 7008L), B4 = c(4960L,
4964L, 4540L, 4164L, 4412L, 4608L, 4628L, 4588L, 4416L, 4312L,
4372L, 3806L, 3652L, 3570L, 3480L, 3708L, 3886L, 4188L, 4284L,
4344L, 4704L, 4776L, 4772L, 4980L, 5000L, 4852L, 4508L, 4916L,
5356L, 5400L, 5268L, 5156L, 4620L, 4324L, 4016L, 3884L, 3854L,
3854L, 3770L, 3562L, 3708L, 3854L, 4084L, 4228L, 4440L, 4532L,
4784L, 5008L, 5292L, 5464L, 4868L, 4996L, 4908L, 4932L, 5060L,
5136L, 5280L, 5444L, 5492L, 5500L, 5560L, 5604L, 5704L, 5660L,
5716L, 5892L, 5844L, 5796L, 5752L, 5816L, 5892L, 6500L, 6488L,
6212L, 5928L, 5796L, 5876L, 6084L, 6284L, 6660L, 6424L, 6088L,
6004L, 6044L, 6268L, 6336L, 6664L, 6500L, 6308L, 6152L, 6288L,
6424L, 6376L, 6860L, 6464L, 6500L, 6508L, 6468L, 7144L, 7652L
), B5 = c(5554L, 5554L, 4782L, 4736L, 4736L, 5018L, 5018L, 4968L,
4968L, 4677L, 4677L, 3814L, 3667L, 3667L, 3594L, 3975L, 3975L,
4348L, 4348L, 4736L, 4736L, 5018L, 5018L, 4968L, 4968L, 4677L,
4677L, 4930L, 5524L, 5524L, 5229L, 5229L, 4424L, 4424L, 4113L,
4113L, 4069L, 4069L, 3857L, 3932L, 3932L, 4228L, 4228L, 4591L,
4591L, 4918L, 4918L, 5324L, 5324L, 5543L, 5327L, 5327L, 5301L,
5471L, 5471L, 5301L, 5471L, 5471L, 5846L, 5977L, 5977L, 5899L,
5899L, 6099L, 6099L, 5977L, 5977L, 5899L, 5899L, 6099L, 6099L,
6857L, 6517L, 6517L, 6220L, 6220L, 6418L, 6418L, 6969L, 6517L,
6517L, 6220L, 6220L, 6418L, 6418L, 6969L, 6969L, 6861L, 6581L,
6581L, 6729L, 6729L, 7265L, 7265L, 6581L, 6729L, 6729L, 7265L,
7265L, 8025L), B6 = c(5249L, 5249L, 4428L, 4553L, 4553L, 4832L,
4832L, 4741L, 4741L, 4428L, 4428L, 3736L, 3464L, 3464L, 3509L,
3894L, 3894L, 4270L, 4270L, 4553L, 4553L, 4832L, 4832L, 4741L,
4741L, 4428L, 4428L, 5030L, 5441L, 5441L, 4926L, 4926L, 4146L,
4146L, 3907L, 3907L, 3910L, 3910L, 3721L, 3831L, 3831L, 4201L,
4201L, 4509L, 4509L, 4871L, 4871L, 5235L, 5235L, 5217L, 5207L,
5207L, 5087L, 5290L, 5290L, 5087L, 5290L, 5290L, 5777L, 5721L,
5721L, 5746L, 5746L, 5982L, 5982L, 5721L, 5721L, 5746L, 5746L,
5982L, 5982L, 6504L, 6116L, 6116L, 5946L, 5946L, 6257L, 6257L,
6916L, 6116L, 6116L, 5946L, 5946L, 6257L, 6257L, 6916L, 6916L,
6407L, 6293L, 6293L, 6545L, 6545L, 7197L, 7197L, 6293L, 6545L,
6545L, 7197L, 7197L, 7998L), B7 = c(4893L, 4893L, 4138L, 4527L,
4527L, 4681L, 4681L, 4505L, 4505L, 4170L, 4170L, 3629L, 3388L,
3388L, 3545L, 3982L, 3982L, 4288L, 4288L, 4527L, 4527L, 4681L,
4681L, 4505L, 4505L, 4170L, 4170L, 5127L, 5268L, 5268L, 4703L,
4703L, 3996L, 3996L, 3775L, 3775L, 3713L, 3713L, 3594L, 3813L,
3813L, 4166L, 4166L, 4462L, 4462L, 4836L, 4836L, 5277L, 5277L,
4910L, 5235L, 5235L, 5001L, 5241L, 5241L, 5001L, 5241L, 5241L,
5688L, 5539L, 5539L, 5599L, 5599L, 5988L, 5988L, 5539L, 5539L,
5599L, 5599L, 5988L, 5988L, 6278L, 5864L, 5864L, 5931L, 5931L,
6177L, 6177L, 6896L, 5864L, 5864L, 5931L, 5931L, 6177L, 6177L,
6896L, 6896L, 6212L, 6159L, 6159L, 6382L, 6382L, 7383L, 7383L,
6159L, 6382L, 6382L, 7383L, 7383L, 7856L), B8 = c(4836L, 4840L,
5044L, 4074L, 4236L, 4404L, 4592L, 4668L, 4796L, 4628L, 4632L,
3914L, 3896L, 3796L, 3580L, 3598L, 3596L, 3830L, 4096L, 4320L,
4460L, 4648L, 4904L, 4980L, 4940L, 5148L, 5180L, 4164L, 4628L,
5304L, 5512L, 5592L, 5500L, 5216L, 4732L, 4380L, 4036L, 4008L,
3994L, 3784L, 3660L, 3650L, 3794L, 4018L, 4212L, 4296L, 4516L,
4648L, 4908L, 5148L, 4876L, 4828L, 4936L, 4992L, 5052L, 5236L,
5236L, 5336L, 5380L, 5708L, 5648L, 5624L, 5580L, 5724L, 5796L,
6280L, 6156L, 6012L, 5876L, 5864L, 5868L, 6488L, 6636L, 6752L,
6612L, 6268L, 5924L, 5968L, 6092L, 6752L, 6876L, 6764L, 6484L,
6176L, 6108L, 6300L, 6460L, 6924L, 6724L, 6528L, 6484L, 6352L,
6500L, 6600L, 6784L, 6876L, 6676L, 6616L, 6732L, 6896L), B8A = c(4679L,
4679L, 4098L, 4524L, 4524L, 4643L, 4643L, 4460L, 4460L, 3987L,
3987L, 3413L, 3294L, 3294L, 3490L, 3840L, 3840L, 4140L, 4140L,
4524L, 4524L, 4643L, 4643L, 4460L, 4460L, 3987L, 3987L, 5232L,
5152L, 5152L, 4421L, 4421L, 3863L, 3863L, 3697L, 3697L, 3561L,
3561L, 3558L, 3788L, 3788L, 4110L, 4110L, 4493L, 4493L, 4894L,
4894L, 5032L, 5032L, 4606L, 5209L, 5209L, 4889L, 5233L, 5233L,
4889L, 5233L, 5233L, 5787L, 5324L, 5324L, 5492L, 5492L, 6018L,
6018L, 5324L, 5324L, 5492L, 5492L, 6018L, 6018L, 5872L, 5544L,
5544L, 5876L, 5876L, 6279L, 6279L, 6963L, 5544L, 5544L, 5876L,
5876L, 6279L, 6279L, 6963L, 6963L, 6134L, 6187L, 6187L, 6547L,
6547L, 7280L, 7280L, 6187L, 6547L, 6547L, 7280L, 7280L, 7968L
), B9 = c(6752L, 6752L, 6752L, 7098L, 7098L, 7098L, 7098L, 6752L,
6752L, 6752L, 6752L, 4997L, 5626L, 5626L, 5626L, 5626L, 5626L,
7098L, 7098L, 7098L, 7098L, 7098L, 7098L, 6752L, 6752L, 6752L,
6752L, 5698L, 5698L, 5698L, 5698L, 5698L, 5408L, 5408L, 5408L,
5408L, 5408L, 5408L, 5685L, 5685L, 5685L, 5685L, 5685L, 6352L,
6352L, 6352L, 6352L, 6352L, 6352L, 5794L, 8407L, 8407L, 7048L,
8407L, 8407L, 7048L, 8407L, 8407L, 8407L, 7048L, 7048L, 8407L,
8407L, 8407L, 8407L, 7048L, 7048L, 8407L, 8407L, 8407L, 8407L,
8487L, 8487L, 8487L, 9610L, 9610L, 9610L, 9610L, 9610L, 8487L,
8487L, 9610L, 9610L, 9610L, 9610L, 9610L, 9610L, 8487L, 9610L,
9610L, 9610L, 9610L, 9610L, 9610L, 9610L, 9610L, 9610L, 9610L,
9610L, 10128L), B10 = c(4170L, 4170L, 3407L, 3301L, 3301L, 3612L,
3612L, 3600L, 3600L, 3352L, 3352L, 2502L, 2388L, 2388L, 2403L,
2659L, 2659L, 2979L, 2979L, 3301L, 3301L, 3612L, 3612L, 3600L,
3600L, 3352L, 3352L, 3856L, 4192L, 4192L, 3840L, 3840L, 3187L,
3187L, 2782L, 2782L, 2634L, 2634L, 2501L, 2562L, 2562L, 2792L,
2792L, 3068L, 3068L, 3405L, 3405L, 3816L, 3816L, 3858L, 3343L,
3343L, 3188L, 3438L, 3438L, 3188L, 3438L, 3438L, 3774L, 3543L,
3543L, 3653L, 3653L, 3934L, 3934L, 3543L, 3543L, 3653L, 3653L,
3934L, 3934L, 4224L, 3999L, 3999L, 3881L, 3881L, 4162L, 4162L,
4724L, 3999L, 3999L, 3881L, 3881L, 4162L, 4162L, 4724L, 4724L,
4293L, 4161L, 4161L, 4380L, 4380L, 5052L, 5052L, 4161L, 4380L,
4380L, 5052L, 5052L, 5756L), B11 = c(3124L, 3124L, 2514L, 2969L,
2969L, 3137L, 3137L, 2922L, 2922L, 2487L, 2487L, 1850L, 1822L,
1822L, 2014L, 2309L, 2309L, 2600L, 2600L, 2969L, 2969L, 3137L,
3137L, 2922L, 2922L, 2487L, 2487L, 3753L, 3535L, 3535L, 2823L,
2823L, 2296L, 2296L, 2151L, 2151L, 1974L, 1974L, 1924L, 2100L,
2100L, 2386L, 2386L, 2731L, 2731L, 3087L, 3087L, 3380L, 3380L,
3006L, 2740L, 2740L, 2443L, 2800L, 2800L, 2443L, 2800L, 2800L,
3228L, 2669L, 2669L, 2935L, 2935L, 3338L, 3338L, 2669L, 2669L,
2935L, 2935L, 3338L, 3338L, 3049L, 2893L, 2893L, 3087L, 3087L,
3550L, 3550L, 4235L, 2893L, 2893L, 3087L, 3087L, 3550L, 3550L,
4235L, 4235L, 3184L, 3242L, 3242L, 3774L, 3774L, 4487L, 4487L,
3242L, 3774L, 3774L, 4487L, 4487L, 5217L), B = c(20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L), E = c(20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L), C = c(20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L), OC = c(30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L
), OLS = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L)), class = "data.frame", row.names = c(NA, -100L
))
Five column with depended vars (B,E, C,OC,OLS). I must perform 5 boosting models. Simple i can do it. Take this script, and just change target var 5 times(any dep var ~. )
# train GBM model
gbm.fit.final109 <- gbm(
formula = Value ~ .,
distribution = "gaussian",
data = new,
n.trees = 483,
interaction.depth = 5,
shrinkage = 0.1,
n.minobsinnode = 5,
bag.fraction = .65,
train.fraction = 1,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)
However, is it possible to create some kind of loop that substitutes column with depended var from the list into the script? For example (mylistwithdepended_vars=c((B,E,C,OC,OLS) ), IEwe take each dependent variable, as soon as it works out in a loop, we substitute another from the list, until through this loop we work out all the columns.
How can i do it. As output i just want 5 models
gbm.fit.finalB
gbm.fit.finalE
gbm.fit.finalC
gbm.fit.finalOC
gbm.fit.finalOLS
Always appreciate your help, thanks.
2 answers
solution1
2 ACCPTED 2022-03-11 09:33:16
Are you looking for something like the following?
library(gbm)
#> Loaded gbm 2.1.8
dependend <- c("B", "E", "C", "OC", "OLS")
mylistwithdepended_vars <- mget(dependend, envir = as.environment(new))
fun <- function(Value){
gbm.fit.final109 <- gbm(
formula = Value ~ .,
distribution = "gaussian",
data = new,
n.trees = 483,
interaction.depth = 5,
shrinkage = 0.1,
n.minobsinnode = 5,
bag.fraction = .65,
train.fraction = 1,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)
}
gbm_models <- Map(fun, mylistwithdepended_vars)
old_par <- par(mfrow = c(1, 5))
mapply(summary, gbm_models, main = names(gbm_models))
#> B E C OC OLS
#> var character,17 character,17 character,17 character,17 character,17
#> rel.inf numeric,17 numeric,17 numeric,17 numeric,17 numeric,17
par(old_par)
Created on 2022-03-11 by the reprex package (v2.0.1)
solution2
2 2022-03-12 17:31:07
The accepted solution fits a mysterious set of 5 models (and not the models the OP has in mind) because it's not clear what the predictors are in the formula Value ~. .
It's better to be explicit what the models, the response and the predictors are.
library("gbm")
#> Loaded gbm 2.1.8
library("tidyverse")
data <- tibble(new)
# Let's define a function to fit a model ...
model_fn <- function(formula, data) {
# Generalized Boosted Regression
gbm(
formula = formula,
distribution = "gaussian",
data = data,
n.trees = 483,
interaction.depth = 5,
shrinkage = 0.1,
n.minobsinnode = 5,
bag.fraction = .65,
train.fraction = 1,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)
}
# ... as well as a function to extract information from the fitted model.
summary_fn <- function(fit) {
# Generalized Boosted Regression
summary(fit, plotit = FALSE) %>%
as_tibble()
}
model_summaries <-
data %>%
pivot_longer(
# We want to fit 5 models, one for each of the following responses:
c(B, C, E, OC, OLS),
names_to = "response",
values_to = "y"
) %>%
nest_by(response) %>%
mutate(
fit = list(model_fn(
# For each response we want to use variables B1 through B11 as predictors.
y ~ B1 + B2 + B3 + B4 + B5 + B6 + B7 + B8 + B9 + B10 + B11,
data = data
))
) %>%
summarise(
summary_fn(fit)
)
#> `summarise()` has grouped output by 'response'. You can override using the
#> `.groups` argument.
model_summaries
#> # A tibble: 55 × 3
#> # Groups: response [5]
#> response var rel.inf
#> <chr> <chr> <dbl>
#> 1 B B1 1 e+ 2
#> 2 B B2 4.11e-29
#> 3 B B8 1.17e-29
#> 4 B B3 8.67e-30
#> 5 B B4 6.88e-30
#> 6 B B11 5.43e-30
#> 7 B B5 2.85e-30
#> 8 B B7 6.14e-31
#> 9 B B10 3.82e-31
#> 10 B B6 1.84e-31
#> # … with 45 more rows
PS. The gbm models fit your data very poorly. Are you sure that the response is Gaussian? It's easy to check that each response takes two unique values, and that each value is observed exactly 50 times.
data %>%
pivot_longer(
# We want to fit 5 models, one for each of the following responses:
c(B, C, E, OC, OLS),
names_to = "response",
values_to = "y"
) %>%
count(
response, y
)
#> # A tibble: 10 × 3
#> response y n
#> <chr> <int> <int>
#> 1 B 20 50
#> 2 B 50 50
#> 3 C 10 50
#> 4 C 20 50
#> 5 E 20 50
#> 6 E 30 50
#> 7 OC 10 50
#> 8 OC 30 50
#> 9 OLS 0 50
#> 10 OLS 10 50
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